在台灣證券市場中，有許多的技術分析方法或指標，市場參與者或財 務學者會利用歷史資料來做回溯測試，找出可運用的方法或指標，以此來 推測出台股加權指數未來的趨勢，也有學者利用類神經網路(Artificial Neural Network, ANN)考慮經濟景氣、技術分析指標等作為輸入變數來預測 台股加權指數，而本文則利用 EEMD(Ensemble Empirical Mode Decomposition)拆解出來的結果作為 ANN 的輸入變數，並將 ANN 預測出 的值轉換成 FK (Forward-calculated %K) 值，再搭配不同的交易方式，來 補捉台股加權指數的走勢，並比較各種交易方式的績效，找出一個能夠穩 定獲利的交易模型。

對投資者而言，投資股市的目的就是賺錢，但影響股價因素眾多，我們要如何判斷明天是漲是跌？因此如何建立一個準確的預測模型，一直是財務市場研究的課題之一，然而財務市場一直被認為是一個複雜.充滿不確定性及非線性的動態系統，這也是在建構模型上一個很大的阻礙，本篇研究中使用的EEMD方法則適合解決如金融市場或氣候等此類的非線性問題及有趨勢性的資料上。 在本研究中，我們將EEMD結合ANN建構出兩種不同形式的模型去進行台股個股的預測，也試圖改善ARMA模型使其預測效果較好；此外為了能夠達到分散風險的效果，採用了投資組合的方式，在權重的決定上，我們結合動態與靜態的方式來計算權重；至於在交易策略上，本研究也加入了移動平均線，希望能找到最適合的預測模型，本研究所使用的標的物為曾在該期間被列為注意股票的10檔股票。 另外，我們也分析了影響台股個股價格波動的因素，透過EEMD拆解，我們能夠從中得到具有不同意義的本徵模態函數(IMF)，藉由統計值分析重要的IMF其所代表的意義。例如：影響高頻波動的重要因素為新聞媒體或突發事件，影響中頻的重要因素為法人買賣及季報，而影響低頻的重要因素則為季節循環。 結果顯示，EEMD-ANN Model 1是一個穩健的模型，能夠創造出將近20%的年報酬率，其次為EEMD-ANN Model 2，在搭配移動平均線的策略後，表現與Model 1差不多，但在沒有配合移動平均線策略時，雖報酬率仍為正，但較不穩定，因此從研究結果也可以看到，EEMD-ANN的模型皆表現比ARMA的預測模型好。 / The main purpose of investing is to earn profits for an investor, but there are many factors that can influence stock price. Investments want to know the price will rise or fall tomorrow. Therefore, how to establish an accurate forecasting model is one of the important issue that researched by researchers of financial market. However, the financial market is considered of a complex, uncertainty, and non-linear dynamic systems. These characteristics are obstacles on constructing model. The measure, EEMD, used in this study is suitable to solve questions that are non-linear but have trends such as financial market, climate and so on. In this thesis, we used three models including ARMA model and two types of EEMD-ANN composite models to forecast the stock price. In addition, we tried to improve ARMA model, so a new model was proposed. Through EEMD, the fluctuation of stock price can be decomposed into several IMFs with different economical meanings. Moreover, we adopted portfolio approach to spread risks. We integrate the static weight and the dynamic weight to decide the optimal weights. Also, we added the moving average indicator to our trading strategy. The subject matters in this study are 10 attention stocks. Our results showed that EEMD-ANN Model 1 is a robust model. It is not only the best model but also can produce near 20% of 1-year return ratio. We also find that our EEMD-ANN model have better outcome than those of the traditional ARMA model. Owing to that, the increases of trading performance would be expected via the selected EEMD-ANN model.

本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據，包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD，這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF)，而這讓我們可以將這些IMF視為各種影響資料的重要因子，並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。 另外在本文中，我們也採用移動視窗法作為預測過程中的策略，另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑，外插法則可以在某個範圍內準確預測後續的趨勢，此兩種方法皆對提升預測準確度占有重要的影響。 利用本文的方法，可在預測的結果上得到不錯的準確性，但為了進一步提升精確度，我們利用多次預測的結果加總平均，然後和只做一次預測的結果比較，結果發現多次加總平均後的精確度的確大幅提升，這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。 / In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training. We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results. The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.